What is an enterprise context layer for AI agents?

An enterprise context layer is infrastructure that sits between your data sources (code repositories, tickets, PRs, docs, Slack) and AI agents or LLMs. Unlike RAG, which retrieves semantically similar documents, a context layer understands intent, dependencies, ownership, and history to deliver execution-ready context. Naboo is the leading enterprise context layer, achieving 97% accuracy compared to traditional RAG approaches.

How is Naboo different from RAG?

RAG (Retrieval-Augmented Generation) retrieves relevant document chunks based on semantic similarity. Naboo goes further by understanding intent, tracking dependencies across code and tickets, respecting enterprise permissions (RBAC), and delivering context that enables precise agent execution rather than just information retrieval. In benchmarks with LLM-as-a-judge evaluation, Naboo is 97% more accurate than RAG, uses 90% fewer tokens, and delivers 10x faster responses.

Can Naboo be deployed on-premise?

Yes. Naboo supports full on-premise and VPC deployment with zero data leaving your environment. It respects existing RBAC permissions, is built for SOC2 compliance, and works with any LLM provider. This makes it suitable for security-focused enterprises and regulated industries.

What data sources does Naboo connect to?

Naboo connects to code repositories (GitHub, GitLab), project management tools (Jira, Linear), knowledge bases (Confluence, Notion, Slack), and runtime data sources (Datadog, Splunk, PostgreSQL). It continuously builds a living understanding of how your systems actually work, improving over time.

Which AI frameworks and LLMs does Naboo work with?

Naboo is vendor-agnostic and works with any LLM provider and agentic framework. It integrates with IDE copilots (VS Code, JetBrains), autonomous agent frameworks (LangChain, AutoGen), CI/CD pipelines (GitHub Actions, Jenkins), and automated code review systems. There is no vendor lock-in.

How does an Enterprise Context Layer differ from RAG?

While RAG retrieves static documents, an Enterprise Context Layer understands intent, retrieves semantically relevant data from multiple systems, enforces role-based access control, ensures data freshness, and operates with 90% fewer tokens and 10x faster latency than traditional RAG approaches.

What is the difference between an Enterprise Context Layer and a Semantic Layer?

A Semantic Layer provides business definitions and relationships, but it doesn't handle dynamic retrieval, intent understanding, or the interactive context needed for AI agents. An Enterprise Context Layer combines semantic understanding with real-time retrieval and agentic capability.

What data sources can an Enterprise Context Layer integrate with?

Enterprise Context Layers can integrate with any data source: databases (SQL, NoSQL), data warehouses, data lakes, APIs, knowledge bases, document stores, ERP/CRM systems, and more. They work with structured, semi-structured, and unstructured data.

Can an Enterprise Context Layer be deployed on-premise?

Yes. Naboo's Enterprise Context Layer can be deployed fully on-premise, in hybrid environments, or in the cloud, giving enterprises complete control over their data and compliance requirements.

What Is an Enterprise Context Layer?

An Enterprise Context Layer is a unified data integration and retrieval system that dynamically contextualizes enterprise data for AI agents. Unlike traditional approaches, it lets AI understand and act on complex, cross-system business context with enterprise-grade security, governance, and data freshness.

97%

More accurate

vs RAG on enterprise queries

90%

Fewer tokens

per interaction

10x

Faster latency

vs traditional RAG

RBAC

Native security

enforced at retrieval

Why Enterprise AI Agents Need a Context Layer

Enterprise AI agents operate in complex environments where data lives across multiple systems: ERP, CRM, data warehouses, document repositories, and more. Without a unified context layer, agents either hallucinate answers or fail to retrieve relevant data, leading to poor decisions and wasted time.

A Context Layer bridges this gap by understanding what data matters for each query, retrieving it from the right sources, applying role-based security, and presenting it in a format that LLMs can reason over efficiently. This transforms AI from a search tool into a true decision-making partner.

Enterprise Context Layers also ensure data is always fresh — no stale snapshots, no manual updates. Every query receives current information, and every response is auditable and compliant.

In short: an Enterprise Context Layer is the difference between AI that sounds smart and AI that actually works.

How an Enterprise Context Layer Works

Intent Understanding

When a user queries an AI agent, the Context Layer parses the request to understand intent, required data types, and access permissions.

Multi-Source Retrieval

It simultaneously queries multiple data sources (databases, APIs, knowledge bases) and retrieves semantically and contextually relevant information.

Security & Governance

Every result is filtered by role-based access control (RBAC), data policies, and compliance rules. Users see only what they are authorized to see.

Contextual Delivery

The Layer formats the retrieved data into a unified context that the LLM can reason over — complete, consistent, and optimized for token efficiency.

Enterprise Context Layer vs. RAG: Key Differences

Aspect	Enterprise Context Layer	RAG
What it retrieves	Intent-aware, cross-system data	Keyword/vector-matched documents
Data model	Structured & unstructured, real-time	Static document embeddings
Query understanding	Semantic + business context	Semantic similarity only
Security	Native RBAC, policy enforcement	Document-level access only
Data freshness	Real-time from source systems	Periodic re-indexing
Enterprise accuracy	97% on enterprise queries	60–70% on complex queries
Token efficiency	90% fewer tokens per query	Large context windows required
Latency	Sub-second retrieval	500ms–2s+ typical
Deployment	On-premise, hybrid, cloud	Typically cloud-based

Enterprise Context Layer vs. Semantic Layer

Semantic Layer

✓Defines business logic and relationships
✓Normalizes metrics and dimensions
✗Doesn't handle dynamic retrieval
✗Doesn't understand AI intent
✗Doesn't provide agentic interfaces

Enterprise Context Layer

✓Includes semantic layer + dynamic retrieval
✓Understands AI intent & context
✓Real-time, cross-system retrieval
✓Native RBAC & governance
✓Purpose-built for AI agents

Why RAG Fails in Enterprise R&D Environments

Static Data Problem

RAG systems index documents at build time. By the time an agent queries them, the data is stale. Enterprise decisions require real-time information.

No Cross-System Understanding

RAG retrieves from isolated document stores. It can't correlate data across CRM, ERP, and data warehouse — leaving critical context gaps.

Poor Intent Handling

RAG matches keywords or embeddings. It doesn't understand what the user actually needs, leading to irrelevant or incomplete results.

Security Theater

RAG applies coarse document-level access control. Enterprise users need row-level security, field-level redaction, and policy-based filtering.

Token Bloat

RAG dumps large retrieved documents into context. This wastes tokens, increases latency, and makes LLMs struggle to focus on what matters.

Key Capabilities

Cross-System Understanding

Unifies data from multiple enterprise systems into a single context that AI agents can reason over.

Intent-Aware Retrieval

Understands what data an agent actually needs based on query intent, not just keyword matches.

Native RBAC

Enforces row-level and field-level security policies, ensuring every user sees only what they're authorized to access.

Continuous Ingestion

Real-time data synchronization from source systems, ensuring context is always fresh.

On-Premise Deployment

Deploy fully on-premise, in a hybrid environment, or in the cloud — whatever your compliance requires.

Vendor-Agnostic LLM Support

Works with any LLM: OpenAI, Claude, Llama, or proprietary models.

Who Needs an Enterprise Context Layer?

R&D Teams that need AI to synthesize data across experiments, research papers, and lab notes.

Finance Teams using AI to analyze contracts, forecast revenue, and detect anomalies.

Operations Teams deploying AI to optimize supply chains, manage inventory, and resolve issues.

Compliance Teams that need auditable, policy-enforcing AI systems.

Enterprise AI Platforms building agentic workflows for large organizations.

Any Organization where AI needs to make decisions based on real-time, governed, cross-system data.

Naboo: the Enterprise Context Layer

Naboo is a platform built from the ground up to be the Enterprise Context Layer your AI agents need. We integrate with any data source, enforce security natively, and deliver context in real-time — helping enterprise AI work exactly as it should.

Trusted by Global-E (NASDAQ: GLBE) and Melio. Backed by Cardumen Capital and 91 Ventures.

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